Locally Advanced Gastric CancerNCT06979817
Machine Learning Model Guided by TLS Predicts Survival and Immune Features in Gastric Cancer
An observational study of Locally Advanced Gastric Cancer and Tumor Immune Microenvironment, run by Qun Zhao.
Trial summary
This study aims to develop and validate a machine learning model that uses information from tertiary lymphoid structures (TLSs)-specialized immune-related cell clusters found near tumors-to predict survival outcomes and immune characteristics in patients with locally advanced gastric cancer. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Locally Advanced Gastric Cancer, Tumor Immune Microenvironment, Tertiary Lymphoid Structures (TLS)
- What is being tested?
- TLS-Informed Machine Learning Prognostic Model
- How many people
- 1,200 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2012to 1 Jan 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- Adult, Older adult
Study population: This study will include patients with histologically confirmed locally advanced gastric adenocarcinoma who have undergone curative-intent surgical resection at participating medical centers. The population will consist of both retrospective and prospective cohorts, with all patients having available tumor tissue for TLS analysis and complete clinical, pathological, and follow-up data. The study aims to capture a representative sample of real-world gastric cancer patients, reflecting a diversity of clinical characteristics, treatment modalities, and outcomes.
Who may be able to join
- Histologically confirmed locally advanced gastric adenocarcinoma (clinical stage cT2-T4 and/or N+)
- Underwent curative-intent gastrectomy (with or without neoadjuvant therapy)
- Availability of adequate tumor tissue specimens for TLS assessment via digital pathology
- Complete baseline clinical, pathological, and follow-up data
- Age ≥ 18 years
- Written informed consent provided (if prospective study component is included)
Who cannot join
- Distant metastases at the time of diagnosis or surgery (M1 stage)
- Prior history of other malignancies within the past 5 years, except for adequately treated in situ carcinoma or non-melanoma skin cancer
- Incomplete or missing essential clinical, pathological, or survival data
- Poor-quality tissue samples not suitable for TLS quantification or digital analysis
- Participation in another clinical trial that may interfere with the study outcomes
About this study
Official title: TLS-Informed Machine Learning Model Predicts Survival and Immune Landscape in Locally Advanced Gastric Cancer
This study aims to develop and validate a machine learning model that uses information from tertiary lymphoid structures (TLSs)-specialized immune-related cell clusters found near tumors-to predict survival outcomes and immune characteristics in patients with locally advanced gastric cancer. By analyzing clinical data, pathology, and imaging results, the model may help doctors better understand a patient's prognosis and personalize treatment strategies. The study will also explore how TLS-related immune patterns relate to the effectiveness of certain therapies, potentially offering new insights for immune-based treatment planning.
- Conditions
- Locally Advanced Gastric Cancer, Tumor Immune Microenvironment, Tertiary Lymphoid Structures (TLS)
- Being tested
- TLS-Informed Machine Learning Prognostic Model
- Medical subject headings
- Tertiary Lymphoid Structures
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
TLS-Informed Machine Learning Prognostic ModelOther
This intervention involves the development and application of a machine learning-based prognostic model that integrates features derived from tertiary lymphoid structures (TLSs) identified in tumor pathology slides, along with clinical and immunological data, to predict overall survival and immune landscape in patients with locally advanced gastric cancer. The model utilizes digital pathology, image analysis, and advanced computational algorithms to quantify TLS-related characteristics and correlate them with patient outcomes. It is designed to stratify patients into risk groups and provide insight into the tumor immune microenvironment, aiming to support personalized treatment planning.
| Study group | Role | What they receive |
|---|---|---|
| Locally Advanced Gastric Cancer Patients | Not described Receives: Other: TLS-Informed Machine Learning Prognostic Model |
What is being measured
The primary outcome is the main question the study is designed to answer.
Overall Survival Predicted by TLS-Informed Machine Learning Model
Measured: Up to 5 Years Post-Surgery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 May 2025
- Study start1 Jan 2012
- Primary completion1 Jan 2024
- Study completion1 Jan 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Locally Advanced Gastric CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,200 participants (actual)
Sponsor and investigators
- Lead sponsor
- Qun Zhao
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Qun Zhao, Hebei Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06979817
- Sponsor's protocol ID
- GC-RAD-AI-2025-03
- Status last verified
- May 2025
- First submitted
- 12 May 2025
- Last updated
- 20 May 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 20 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- TLS-Informed Machine Learning Model Predicts Survival and Immune Landscape in Locally Advanced Gastric Cancer
- ClinicalTrials.gov ID
- NCT06979817
- Acronym / sponsor ID
- GC-RAD-AI-2025-03
- Conditions
- Locally Advanced Gastric Cancer, Tumor Immune Microenvironment, Tertiary Lymphoid Structures (TLS)
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,200 participants (actual)
- Lead sponsor
- Qun ZhaoUniversity, hospital or other
- Study start
- 1 Jan 2012 (actual)
- Primary completion
- 1 Jan 2024 (actual)
- Study completion
- 1 Jan 2024 (actual)
- First posted
- 20 May 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This study aims to develop and validate a machine learning model that uses information from tertiary lymphoid structures (TLSs)-specialized immune-related cell clusters found near tumors-to predict survival outcomes and immune characteristics in patients with locally advanced gastric cancer. By analyzing clinical data, pathology, and imaging results, the model may help doctors better understand a patient's prognosis and personalize treatment strategies. The study will also explore how TLS-related immune patterns relate to the effectiveness of certain therapies, potentially offering new insights for immune-based treatment planning.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Locally Advanced Gastric Cancer Patients | – | Other: TLS-Informed Machine Learning Prognostic Model |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- This study will include patients with histologically confirmed locally advanced gastric adenocarcinoma who have undergone curative-intent surgical resection at participating medical centers. The population will consist of both retrospective and prospective cohorts, with all patients having available tumor tissue for TLS analysis and complete clinical, pathological, and follow-up data. The study aims to capture a representative sample of real-world gastric cancer patients, reflecting a diversity of clinical characteristics, treatment modalities, and outcomes.
Key inclusion criteria (6 listed)
- Histologically confirmed locally advanced gastric adenocarcinoma (clinical stage cT2-T4 and/or N+)
- Underwent curative-intent gastrectomy (with or without neoadjuvant therapy)
- Availability of adequate tumor tissue specimens for TLS assessment via digital pathology
- Complete baseline clinical, pathological, and follow-up data
- Age ≥ 18 years
Key exclusion criteria (5 listed)
- Distant metastases at the time of diagnosis or surgery (M1 stage)
- Prior history of other malignancies within the past 5 years, except for adequately treated in situ carcinoma or non-melanoma skin cancer
- Incomplete or missing essential clinical, pathological, or survival data
- Poor-quality tissue samples not suitable for TLS quantification or digital analysis
- Participation in another clinical trial that may interfere with the study outcomes
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| TLS-Informed Machine Learning Prognostic ModelThis intervention involves the development and application of a machine learning-based prognostic model that integrates features derived from tertiary lymphoid structures (TLSs) identified in tumor pathology slides, along with clinical and immunological data, to predict overall survival and immune landscape in patients with locally advanced gastric cancer. The model utilizes digital pathology, image analysis, and advanced computational algorithms to quantify TLS-related characteristics and correlate them with patient outcomes. It is designed to stratify patients into risk groups and provide insight into the tumor immune microenvironment, aiming to support personalized treatment planning. | Other | Locally Advanced Gastric Cancer Patients |
Primary outcome
Overall Survival Predicted by TLS-Informed Machine Learning Model
Time frame: Up to 5 Years Post-Surgery
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 May 2025.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Locally Advanced Gastric Cancer and Tumor Immune Microenvironment, run by Qun Zhao.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 20 May 2025. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Overall Survival Predicted by TLS-Informed Machine Learning Model (time frame: Up to 5 Years Post-Surgery). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 May 2025.